Logistics Distribution Vehicle Scheduling System Based On Cloud Platform

The proportion of the logistics industry in the market economy is increasing.To develop the modern logistics industry steadily for a long time,it is necessary to effectively solve the problem of transportation costs and optimize the logistics system.The vehicle scheduling problem is a key link in optimizing the logistics system.The dynamic vehicle scheduling problem involves many uncertain factors and is more complicated to solve,so it has gradually become a research hotspot in recent years.The application of the most popular cloud computing and Internet of Things technology to build a cloud logistics information platform can save a lot of logistics resources and is of great significance to the development of logistics.Aiming at the vehicle scheduling problem in the logistics system,this paper designs a vehicle scheduling system based on the logistics cloud platform.The cloud platform collects and stores logistics data,the vehicle scheduling system implements the display of vehicle information,cargo information,customer information,and vehicle scheduling functions through an interactive interface.The vehicle scheduling 'function uses the constructed scheduling model in combination with the collected logistics information,and uses the solving algorithm to obtain the optimal driving path of the vehicle.In view of the logistics information uncertainty in the logistics distribution vehicle scheduling problem,with the minimum transportation cost as the optimization goal,a dynamic vehicle scheduling mathematical model with distribution time constraints and vehicle load constraints is established;In order to solve the optimal driving route of logistics distribution vehicles,the scheduling model is solved based on genetic algorithm.The quantum genetic algorithm is used to improve the efficiency of the algorithm.A dynamic selection strategy is adopted for the size of the quantum rotation angle to accelerate the convergence of the algorithm.The comparison results of simulation experiments show that the quantum genetic algorithm finds the driving route with lower transportation cost than the genetic algorithm.In terms of algorithm efficiency,the average number of iterations decreases and the convergence speed increases.Quantum coding increases the diversity of the population and improves the global convergence.Quantum rotation accelerates the algorithm convergence.The results prove that the algorithm is suitable for solving dynamic vehicle scheduling problems.Designing a logistics distribution vehicle scheduling system to achieve logistics vehicle scheduling provides a program reference for the actual logistics system.